Bibliographic record
Abstract
World has moved towards the cashless transactions and in this order to using E-money Transaction some countries like Belgium, France, Canada etc. have adopted totally cashless transaction system. I think the first requirement for the cashless transaction is to avoid fake currency and others are easy transaction, transparency, development of E-commerce etc.E money is the basic pillar of cashless transaction and even the people of progressive country are also adopting very frequently in place of hard cash and during Demonetisation the E-money transaction was the main relief for Indian people and even it was used for purchasing the teas, chhole bhatoore , samosas etc.through the E-money transactions via Paytm etc but due to cases of fraud this system is unable to reach its deserving place.The first Question is, what E-money is so E-money is the short form of the Electronic Money, the money which is stored in Electonic Device/modes or used through electronically with the help of internet computerized Server.Basically E-money can be divide in two parts 1. Card based and Network based. Card based E-Money Includes Debit card,Credit Card, ATM card etc. and Network Based E-money includes Paytm, Proton, BHIM app etc. E-money transaction is need of present era but the safety for its user is also very important because 1. Our Cyber system is not ready to protect completely this type of transaction. 2. Users are not aware for their rights and cyber solutions available to them and 3. Carelessness of the users to provide the opportunities tothe cyber-criminal.The use of the E-money is adopting by the people at large but our system like banks government agencies, cyber cells, police etc. are not taking suitable steps as per the ratio and situations. However in order to safety and cyber solutions the Information Technology Act-2000 has been enacted but the lack of skilled officer are also a big problem. In case of Cyber Crime victim can report in the police station under which jurisdiction he reside or where his bank situate or where the incident took place and the matter will be investigated only by the Competent/Designated officer. E-money Users must be aware with tricks of Cyber Criminals and should take safety steps properly with due care and cautions, the Cyber fraudsters/Criminals use phishing, malware, money mule, sim cloning , card fraud , vishing , ATM Skimming and sometime also fraud committed also by the E-money user.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".